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Dan Dawson, Ph.D.

Dan Dawson, Ph.D.
Senior Scientist

Dan Dawson, Ph.D.

Senior Scientist

Dr. Dan Dawson is an ecologist and environmental toxicologist with a general interest in the use of quantitative approaches to understanding and addressing human and ecological problems. With more than 10 years of graduate and post-graduate experience, he has worked with academic institutions, county governments, and state and federal agencies to tackle a diverse set of questions. Along the way, he has acquired expertise in a wide variety of quantitative toolsets, including chemical exposure modeling, population modeling, disease transmission modeling, machine learning/QSAR modeling, and statistical modeling and analysis. Dr. Dawson has published much of his work in the peer-reviewed lite...

Dr. Dan Dawson is an ecologist and environmental toxicologist with a general interest in the use of quantitative approaches to understanding and addressing human and ecological problems. With more than 10 years of graduate and post-graduate experience, he has worked with academic institutions, county governments, and state and federal agencies to tackle a diverse set of questions. Along the way, he has acquired expertise in a wide variety of quantitative toolsets, including chemical exposure modeling, population modeling, disease transmission modeling, machine learning/QSAR modeling, and statistical modeling and analysis. Dr. Dawson has published much of his work in the peer-reviewed literature, and he has frequently presented research at scientific conferences. Finally, he is interested in continually improving science communication and believes that scientific information should be conveyed clearly, concisely, and with as little jargon as possible.

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Data Science

Understanding How Data Structure and Processing Decisions Influence the Output of Transmission Models, North Carolina State University, Raleigh, North Carolina Used a data set of cattle locations in a feedlot and a network-based simulation model of contact-based disease transmission, and investigated how data processing influences the outcome of simulated epidemics. By converting time series of contact data to a measure of information (entropy), demonstrated that different processing decisions have significant implications for resultant simulations. This research was communicated in a peer-reviewed journal article.

Ecological Modeling

Modeling Mosquito Dynamics Using an Agent-Based Model, Lubbock, Texas Created an agent-based, spatially explicit model of mosquito population dynamics. Model allowed for the evaluation of hypotheses regarding the impacts of larvicidal treatment. This research was communicated as part of a Ph.D. dissertation.
Modeling Impacts of Nectar-Based Pesticide to Honeybees Using an Agent-Based Model, Office of Research and Development, EPA Integrated a nectar-based pesticide exposure and effects module into BeeHave, an existing agent-based model of honeybee colony dynamics. The model was calibrated using Approximate Bayesian Computation, and model behavior and structure will be compared with other established models of honeybee colony dynamics. This research will be communicated in upcoming peer-reviewed journal articles.

Human Health Risk Assessment

Exposure Assessment of 1,4-Dioxane from Drinking Water and Product Use, Office of Research and Development, EPA Created an R-based exposure modeling workflow using EPA’s Stochastic Human Exposure Dose Simulator-High Throughput exposure model. This workflow demonstrated that human exposure likely receives greater contribution from contaminated drinking water (when contaminated drinking water occurs) than from the use of personal care products contaminated with 1,4-dioxane. This research was communicated in a peer-reviewed journal article.
Estimating Sources of Surface Water Contamination of 1,4-Dioxane, Office of Research and Development, EPA Developed a novel MS Excel-based model to estimate upstream sources of 1,4-dioxane to downstream surface water drinking plants. This work is being used by the EPA Office of Pollution Prevention and Toxics as part of a re-evaluation of risks posed by 1,4-dioxane.

PFAS

Modeling Half-Lives of PFAS across Multiple Species, Office of Research and Development, EPA Developed a machine learning (random forest) model of serum half-lives (t1/2) of 11 PFAS chemicals across 4 species. Developed in R, the model was training on literature-based t1/2 values, and used a variety of predictors based on hypothesized drivers of PFAS toxicokinetics. This material is being communicated via a peer reviewed journal article.

Statistical Modeling

Modeling Mosquito Populations in Response to Environmental Stressors and Mosquito Control, Tarrant County, Texas Created a regression-based model of mosquito population based on environmental drivers, spatial information, and mosquito control records. This research was communicated in a peer-reviewed journal article.
Modeling Drivers of Bartonella henselae Exposure in Dogs in North Carolina Assisted in the creation of a statistical model providing inference into social, environmental, and spatial drivers of Bartonella henselae infection in dogs. This research was communicated in a peer-reviewed journal article.

Toxicology

Creating Open-Source Models of Toxicokinetic Model Parameters Intrinsic Clearance (Clint) and Fraction Unbound by Protein(fub), Office of Research and Development, EPA Developed publicly available models of TK parameters using open-source data. The models were developed in the program R. Model predictions were used in conjunction with a basic toxicokinetic model to calculate oral equivalent doses, and bioactivity/exposure ratios for chemicals. This research was communicated in a peer-reviewed journal article, and predictions for several thousand chemicals of the Tox21 data set were incorporated into the open-source high-throughput toxicokinetic R package, httk.
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